Network Intrusion Detection System Based on Feature Selection and Triangle Area Support Vector Machine
As the cost of the data processing and Internet accessibility increases, more and more organizations are becoming vulnerable to a wide range of cyber threats. Most current offline intrusion detection systems are focused on unsupervised and supervised machine learning approaches. Existing model has high error rate during the attack classification using support vector machine learning algorithm. Besides, with the study of existing work, feature selection techniques are also essential to improve high efficiency and effectiveness.